Association between Dyslipidaemia and Cognitive Impairment: A Meta-Analysis of Cohort and Case-Control Studies
Bibliographic record
Abstract
Background: This study explored the specific relationship between different lipid indicators and cognitive impairment and aimed to provide a reference for implementing targeted lipid regulation measures to prevent and alleviate cognitive impairment. Methods: We searched three databases (PubMed, Embase, and Web of Science) for literature related to hyperlipidaemia, lipid levels, and cognitive impairment, and used the Newcastle-Ottawa Scale to evaluate the quality of the identified literature. A meta-analysis was performed using RevMan 5.4, and the combined effect size ratio using a random-effects model (odds ratio [OR] and 95% confidence interval [CI]) was used to evaluate the association between dyslipidaemia and cognitive impairment. Results: Among initially identified 2247 papers, we ultimately included 18 studies involving a total of 758,074 patients. The results of the meta-analysis revealed that patients with hyperlipidaemia had a 1.23-fold higher risk of cognitive impairment than those with normal lipid levels (OR = 1.23, 95% CI: 1.04–1.47, p = 0.02). Further subgroup analysis showed that elevated total cholesterol (TC) levels increased the risk of cognitive impairment by 1.59-fold (OR = 1.59, 95% CI: 1.27–2.01, p < 0.0001) and were more significant in older or male patients. Moreover, elevated triglyceride levels were inversely correlated with cognitive disorders, whereas elevated low-density lipoprotein cholesterol levels were unrelated to cognitive impairment risk. Conclusions: Dyslipidaemia was strongly associated with cognitive impairment, and elevated TC levels were a risk factor for cognitive impairment. Furthermore, the damaging effects of elevated TC levels on cognition were more pronounced in older and male populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".